Non-Invasive Detection of Protein Content in Several Types of Plant Feed Materials Using a Hybrid Near Infrared Spectroscopy Model

Integrating portable NIR spectrometry with deep learning for accurate Estimation of crude protein in corn feed

This study investigates the challenges encountered in utilizing portable near-infrared (NIR) spectrometers in agriculture, specifically in developing predictive models with high accuracy and robust generalization abilities despite limited spectral resolution and small sample sizes. The research concentrates on the near-infrared spectra of corn feed, utilizing spectral processing techniques and CNNs to precisely estimate crude protein content. Five preprocessing methods were implemented alongside two-dimensional (2D) correlation spectroscopy, resulting in the development of both one-dimensional (1D) and 2D regression models. A comparative analysis of these models in predicting crude protein content demonstrated that 1D-CNNs exhibited superior predictive performance within the 1D category. For the 2D models, CropNet and CropResNet were utilized, with CropResNet demonstrating more accurate and superior predictive capabilities. Overall, the integration of 2D correlation spectroscopy with suitable preprocessing techniques in deep learning models, particularly the 2D CropResNet, proved to be more precise in predicting the crude protein content in corn feed. This finding emphasis the potential of this approach in the portable spectrometer market.

Combination of LIBS-based elemental analysis and near-infrared molecular fingerprinting of bile juice to enhance identification of gallbladder cancer

LIBS and NIR spectroscopy, enabling the direct measurement of raw bile, were cooperatively combined to enhance the discrimination of gallbladder cancer from other diseases of gallstone and gallbladder polyp.

Enhancing the Quality of Total Mixed Ration Containing Cottonseed or Rapeseed Meal by Optimization of Fermentation Conditions

Cottonseed meal (CSM) and rapeseed meal (RSM) are protein sources in livestock feed. However, the applications of both ingredients are limited in diets due to the existence of anti-nutritional factors such as free gossypol and glucosinolate. The aim of this study was to determine the optimal fermentation conditions for reducing anti-nutritional factors and increasing the nutritional value of fermented total mixed rations containing cottonseed or rapeseed meal. An orthogonal design L9 (34) was performed to optimize the fermentation conditions, including fermentation time, temperature, moisture content and microbial strain. Optimum fermentation conditions were performed using different fermentation times (48, 60, 72 h), fermentation temperatures (28 °C, 32 °C, 36 °C), moisture content (40%, 50%, 60%) and microbial inoculations (1 = Bacillus clausii with 1 × 109 CFU/kg DM for CSM or 1 × 1010 CFU/kg DM for RSM; 2 = Saccharomyces cariocanus with 5 × 109 CFU/kg DM; 3 = mixed strain (B. clausii:S. cariocanus ratio 1:1). The results show that the concentration of free gossypol content was reduced (p < 0.05), while the crude protein content was increased (p < 0.05) in CSM through optimum fermentation conditions: time 60 h; temperature 32 °C; moisture content 50% and inoculated with B. clausii (1 × 109 CFU/kg DM) as well as S. cariocanus (5 × 109 CFU/kg DM). Likewise, the concentration of glucosinolate was lowered (p < 0.05) and the crude protein was increased (p < 0.05) in RSM through optimum fermentation conditions: time 60 h; temperature 28 °C; moisture 50% and inoculated with B. clausii (1 × 1010 CFU/kg DM) as well as S. cariocanus (5 × 109 CFU/kg DM). Our findings indicate that the optimal fermentation conditions of total mixed rations with cottonseed meal or rapeseed meal enhance the nutritional value, thereby making them viable and usable feedstuffs for potential use in livestock industries.

Effects of cultivars and fertilization levels on the quality of rice milling: A diagnosis using near-infrared spectroscopy, X-ray diffraction, and scanning electron microscopy

Cultivars and fertilization levels influence rice productivity and can be associated with grain quality. Thus, it is possible to make decisions regarding the choice of cultivars and application of fertilizer levels based on the type of milling, a necessary post-harvest process that may minimize the nutrient load in the grains and result in loss in quality. This study relates the physicochemical composition and morphological quality of brown and polished milled rice grains, cultivar types, and different levels of soil fertilization using near-infrared spectroscopy analysis, X-ray diffraction and scanning electron microscopy. Statistical tools were used to test the various treatments and identify the relationship between factors and variables. A high fertilization level is related to increasing crude protein composition and starch for cultivar IRGA 431 CL associated with polished rice. However, the combination of cultivar IRGA 424 RI and brown rice demonstrated a higher grain resistance, and different percentages of whole, chalky, and damaged rice. The correlation between ash × crude protein and starch × crude fiber was found to be positive for brown rice and negative for the polished rice. Further, an increase in starch content was inversely proportional to the ash content, whereas an increase in crude protein was inversely proportional to the low-fat content in milled rice. The crystalline characteristics of rice starch were preserved at high fertilization levels associated with polished grains that demonstrated high starch content. Polished grains, however, showed more pores and cavities, and consequently greater permeabilities in the surface. It is recommended that batches of grains produced from cultivar IRGA 431 CL with high levels of fertilization be subjected to polished rice milling to achieve high protein and starch quality. However, grains from cultivar IRGA 424 RI with high levels of fertilization are recommended for brown rice milling owing to the high percentage of physical defects observed.

Selected Instrumental Techniques Applied in Food and Feed: Quality, Safety and Adulteration Analysis

This review presents an overall glance at selected instrumental analytical techniques and methods used in food analysis, focusing on their primary food science research applications. The methods described represent approaches that have already been developed or are currently being implemented in our laboratories. Some techniques are widespread and well known and hence we will focus only in very specific examples, whilst the relatively less common techniques applied in food science are covered in a wider fashion. We made a particular emphasis on the works published on this topic in the last five years. When appropriate, we referred the reader to specialized reports highlighting each technique's principle and focused on said technologies' applications in the food analysis field. Each example forwarded will consider the advantages and limitations of the application. Certain study cases will typify that several of the techniques mentioned are used simultaneously to resolve an issue, support novel data, or gather further information from the food sample.

Detection of Nitrogen Content in Rubber Leaves Using Near-Infrared (NIR) Spectroscopy with Correlation-Based Successive Projections Algorithm (SPA)

Near-infrared spectroscopy is an efficient, low-cost technology that has potential as an accurate method in detecting the nitrogen content of natural rubber leaves. Successive projections algorithm (SPA) is a widely used variable selection method for multivariate calibration, which uses projection operations to select a variable subset with minimum multi-collinearity. However, due to the fluctuation of correlation between variables, high collinearity may still exist in non-adjacent variables of subset obtained by basic SPA. Based on analysis to the correlation matrix of the spectra data, this paper proposed a correlation-based SPA (CB-SPA) to apply the successive projections algorithm in regions with consistent correlation. The result shows that CB-SPA can select variable subsets with more valuable variables and less multi-collinearity. Meanwhile, models established by the CB-SPA subset outperform basic SPA subsets in predicting nitrogen content in terms of both cross-validation and external prediction. Moreover, CB-SPA is assured to be more efficient, for the time cost in its selection procedure is one-twelfth that of the basic SPA.